1. Field
Some example embodiments may relate to position recognition methods of autonomous mobile robots in which the autonomous mobile robots may recognize the current position thereof in stopped states without unnecessary movement of the autonomous mobile robots.
2. Description of Related Art
In general, an autonomous mobile robot refers to a robot which may autonomously cope with an unknown environment without any prior knowledge. Such an autonomous mobile robot is widely used in various fields and, in place of a human, performs operations, for example, helping a handicapped person, transportation of goods in factories, and operation in dangerous environments, such as nuclear waste treatment plants or deep at sea. Moreover, an autonomous mobile robot may be used as an autonomous cleaner or an autonomous lawn mower.
In order to effectively provide services to humans, such an autonomous mobile robot must be capable of recognizing the position of the mobile robot in the environment within which the robot operates, particularly one including humans. Here, there are various position recognition methods of an autonomous mobile robot according to kinds of an environment map provided within the robot. Environment maps may include a feature map and a grid map. Among these environment maps, the grid map is most widely used.
A general position recognition method using the grid map is as follows. That is, positions on the grid map where an autonomous mobile robot will be located are estimated, all range scan data which may be acquired from the estimated positions is simulated, the most similar data to range scan data which is actually collected by the autonomous mobile robot using a range scan sensor is detected from among all simulated range scan data, and the estimated position generated from such data is recognized as the current position of the autonomous mobile robot.
However, in case of such a position recognition method, if the number of estimated positions is small, there is a possibility of failure of position recognition, and if the number of the estimated positions is large, calculation time for position recognition is increased. Further, since the autonomous mobile robot simulates range scan data at corresponding positions while continuously moving along the estimated positions and extracts range scan data similar to the actual range scan data, the above recognition method is ineffective.